Motivated by global analysis of aircraft-based measurements of air pollutants and climate variables, and specifically the COVID-19 pandemic’s possible impact on ozone concentrations, a functional autoregressive model is proposed to capture global spatio-temporal variability, incorporating solar radiation cycles. Efficient estimation techniques are developed and means of suitable visualization demonstrated, paving the way for similar analyses in the future.
Stöcker, Almond; Caponera, Alessia. (2024). Functional autoregressive processes on a spherical domain for global aircraft-based atmospheric measurements. In Proceedings of the Statistics and Data Science 2024 Conference: New Perspectives on Statistics and Data Science (pp. 161- 167). Università degli Studi di Palermo. Isbn: 978-88-5509-645-4. https://unipapress.com/book/proceedings-of-the-statistics-and-data-science-2024-conference/.
Functional autoregressive processes on a spherical domain for global aircraft-based atmospheric measurements
Alessia Caponera
2024
Abstract
Motivated by global analysis of aircraft-based measurements of air pollutants and climate variables, and specifically the COVID-19 pandemic’s possible impact on ozone concentrations, a functional autoregressive model is proposed to capture global spatio-temporal variability, incorporating solar radiation cycles. Efficient estimation techniques are developed and means of suitable visualization demonstrated, paving the way for similar analyses in the future.
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